Description

Structural equation modeling (SEM) and meta-analysis are two powerful statistical methods in the educational, social, behavioral, and medical sciences. They are often treated as two unrelated topics in the literature. This book presents a unified framework on analyzing meta-analytic data within the SEM framework, and illustrates how to conduct meta-analysis using the metaSEM package in the R statistical environment.

Meta-Analysis: A Structural Equation Modeling Approach begins by introducing the importance of SEM and meta-analysis in answering research questions. Key ideas in meta-analysis and SEM are briefly reviewed, and various meta-analytic models are then introduced and linked to the SEM framework. Fixed-, random-, and mixed-effects models in univariate and multivariate meta-analyses, three-level meta-analysis, and meta-analytic structural equation modeling, are introduced. Advanced topics, such as using restricted maximum likelihood estimation method and handling missing covariates, are also covered. Readers will learn a single framework to apply both meta-analysis and SEM. Examples in R and in Mplus are included.

This book will be a valuable resource for statistical and academic researchers and graduate students carrying out meta-analyses, and will also be useful to researchers and statisticians using SEM in biostatistics. Basic knowledge of either SEM or meta-analysis will be helpful in understanding the materials in this book.

6.4 Relationship between the multivariate and the three-level meta-analyses 195

6.5 Illustrations using R 200

6.6 Concluding remarks and further readings 210

7 Meta-analytic structural equation modeling 214

7.1 Introduction 214

7.2 Conventional approaches 218

7.3 Two-stage structural equation modeling: fixed-effects models 223

7.4 Two-stage structural equation modeling: random-effects models 233

7.5 Related issues 235

7.6 Illustrations using R 244

7.7 Concluding remarks and further readings 273

8 Advanced topics in SEM-based meta-analysis 279

8.1 Restricted (or residual) maximum likelihood estimation 279

8.2 Missing values in the moderators 289

8.3 Illustrations using R 294

8.4 Concluding remarks and further readings 309

9 Conducting meta-analysis with Mplus 313

9.1 Introduction 313

9.2 Univariate meta-analysis 314

9.3 Multivariate meta-analysis 327

9.4 Three-level meta-analysis 346

9.5 Concluding remarks and further readings 353

A A brief introduction to R, OpenMx, and metaSEM packages 356

A.1 R 357

A.2 OpenMx 362

A.3 metaSEM 364

References 368

Index 369

"This book will be a valuable resource for statistical and academic researchers and graduate students carrying out meta-analyses, and will also be useful to researchers and statisticians using SEM in biostatistics. cover, would sit well on the bookshelves of those interested in this increasingly important field of scientific endeavour." (Zentralblatt MATH, 1 June 2015)